CN114186785A - Method for constructing house resource endowment map and method for providing house resource information - Google Patents

Method for constructing house resource endowment map and method for providing house resource information Download PDF

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CN114186785A
CN114186785A CN202111305308.3A CN202111305308A CN114186785A CN 114186785 A CN114186785 A CN 114186785A CN 202111305308 A CN202111305308 A CN 202111305308A CN 114186785 A CN114186785 A CN 114186785A
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resource
classification
peripheral
index
information
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朱建平
李秀荣
陈宇晟
张露沁
黄斌斌
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Xiamen Yunzhong Lianda Data Technology Co ltd
Xiamen Yunzhong Lian Technology Co ltd
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Xiamen Yunzhong Lianda Data Technology Co ltd
Xiamen Yunzhong Lian Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/26—Visual data mining; Browsing structured data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9024—Graphs; Linked lists
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/906—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0282—Rating or review of business operators or products
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/16—Real estate

Abstract

The application provides a method for constructing a house resource endowment atlas, which relates to the technical field of information, and comprises the following steps: acquiring peripheral resource data of the housing cell by using a directional information acquisition technology, wherein the peripheral resource data comprises peripheral resource classification information; preprocessing the peripheral resource data to obtain preprocessed data; and constructing a house resource intrinsic map based on the preprocessed data, wherein the house resource intrinsic map comprises house cell resource index information.

Description

Method for constructing house resource endowment map and method for providing house resource information
Technical Field
The application relates to the technical field of information, in particular to a method for constructing a house resource endowment map.
Background
In recent years, with the vigorous development of the real estate industry, the real estate resources circulating in the market are increased day by day. Facing the problem of huge workload and evaluation accuracy when organizations such as massive house resources, house intermediaries and the like perform price evaluation and formulation on houses, consumers also have various personalized requirements and real and comprehensive house related information which is necessary to be referred to for meeting the personalized requirements when selecting houses, wherein a very important type of information is information of the resources around the houses, such as peripheral traffic, commercial service facilities, infrastructure distribution and the like. The traditional method relies on one-sided collection of empirical information, such as landmark buildings, large business circles, transportation hub centers and the like distributed on the periphery, so that peripheral resources which are beneficial to daily life but small in scale, such as convenience stores, drug stores, small stations and the like, are easy to ignore, and evaluation based on the empirical information can cause the consideration of dimensions to be incomplete, and negative dimension information is easy to be hidden by mechanisms such as house intermediaries and the like, so that the condition of information inconsistency is easy to occur, and therefore, the peripheral resource information of the house is difficult to acquire and comprehensively and quantitatively display.
On the other hand, urban rental housing offers a nearly unique housing option for low-to-medium income foreign populations. However, the information of the two parties of the house leasing market is asymmetric, the fish and dragon of the leasing organization are mixed, and the monopoly of the rent pricing right occurs occasionally, so that various disordering phenomena seriously affect the healthy development of the leasing market. Resource endowment, also called element endowment, refers to the production elements including labor force, capital, land, technology and the like owned by one country in the new classical trade theory. The house resources are natural resources and acquired in the future, and the house resources are acquired in the residential area and the residential area. The change of house resources determines the general development trend of house lease price. Different from the direct guiding of price indexes to demands, the natural endowment of resources is the direct support to demands, and the construction and the application of the natural endowment indexes of cities or regions have important significance for standardizing the house lease management behaviors, guiding the healthy development of the house and the land industry and promoting the economic growth.
Disclosure of Invention
Based on the method, the resource information around the house property can be comprehensively acquired through the directional search technology, the house resource endowment atlas is constructed through processing and calculating of information data, and the problems that the resource information around the house property is not comprehensive and opaque and visual impression is difficult to build quickly are solved.
According to an aspect of the present application, a method for constructing a house resource endowment atlas is provided, which includes:
acquiring peripheral resource data of the housing cell by using a directional information acquisition technology, wherein the peripheral resource data comprises peripheral resource classification information;
preprocessing the peripheral resource data to obtain preprocessed data;
and constructing a house resource intrinsic map based on the preprocessed data, wherein the house resource intrinsic map comprises house cell resource index information.
According to some embodiments, the aforementioned method further comprises: obtaining the position information of a housing cell; obtaining a plurality of resource individual information around the housing district based on the position information of the housing district and the surrounding resource classification information, wherein the resource individual information comprises the classification information of resource individuals, the position information of the resource individuals, the distance information of the resource individuals and the comprehensive information of the resource individuals, and the classification information of the resource individuals is from the surrounding resource classification information.
According to some embodiments, the aforementioned method further comprises: the method comprises a primary peripheral resource classification and a secondary peripheral resource classification, wherein the secondary peripheral resource classification is a secondary classification under the primary peripheral resource classification.
According to some embodiments, the aforementioned method further comprises: obtaining an effective distance corresponding to the secondary peripheral resource classification based on the distance information of the resource individuals and the corresponding secondary peripheral resource classification; and screening the resource individual information based on the effective distance to obtain preprocessed data.
According to some embodiments, the aforementioned method further comprises: obtaining distance information of each resource individual and corresponding secondary peripheral resource classification; obtaining the prior effective distance of each secondary peripheral resource classification;
under the same secondary peripheral resource classification, calculating the cumulative distribution of the distance information of the resource individuals in the prior effective distance:
Figure BDA0003339985860000021
wherein the content of the first and second substances,
Figure BDA0003339985860000022
the a priori effective distance of the secondary peripheral resource classification numbered j,
Figure BDA0003339985860000023
and the distance information of the resource individuals with the number of k in the secondary peripheral resource classification with the number of j of the house cell with the number of i.
According to some embodiments, the aforementioned method further comprises: under the same secondary peripheral resource classification: s1: quantitatively increasing the prior effective distance of the secondary peripheral resource classification, judging whether the increased prior effective distance is greater than the administrative division radius, and executing S4 if the increased prior effective distance is greater than the administrative division radius; s2: calculating the cumulative distribution of the distance information of the resource individuals in the increased prior effective distance; s3: comparing the difference between the newly obtained cumulative distribution and the cumulative distribution obtained at the previous time, judging whether the difference is significant, if so, taking the newly obtained prior effective distance as the effective distance, and if not, executing S1; s4: and taking the previous priori effective distance as the effective distance.
According to some embodiments, the aforementioned method further comprises: and screening the resource individual information of the distance information of the resource individual within the effective distance to obtain the preprocessed data.
According to some embodiments, the aforementioned method further comprises: obtaining a secondary classification index corresponding to the secondary peripheral resource classification based on the preprocessed data; obtaining a second-level index weight corresponding to the second-level classification index based on a priori score obtained in advance; and obtaining a primary classification index corresponding to the primary peripheral resource classification based on the secondary classification index and the corresponding secondary index weight, wherein the secondary classification index and the primary classification index are components of the resource index information of the housing district.
According to some embodiments, the aforementioned method further comprises: sorting the distance information of the resource individuals belonging to the same secondary peripheral resource classification in ascending order to obtain a decile number; the secondary peripheral resource classification is further classified into a positive resource type or a negative resource type, and for the resource individuals in the secondary peripheral resource classification of the positive resource type, the resource score of the resource individual is calculated:
Figure BDA0003339985860000031
for the resource individuals in the secondary peripheral resource classification of the negative resource type, calculating resource scores of the resource individuals:
Figure BDA0003339985860000041
wherein the content of the first and second substances,
Figure BDA0003339985860000042
the resource score of the resource individual with the number of k in the secondary peripheral resource classification with the number of j of the house cell with the number of i, djnA decile number with a number of n is included in the distance information of all the resource individuals in ascending order in the secondary peripheral resource classification with a number of j, wherein n is 0,1,2,3,. and 9; obtaining the average resource score of all resource individuals in the same secondary peripheral resource classification based on the resource scores of the resource individuals:
Figure BDA0003339985860000043
wherein the content of the first and second substances,
Figure BDA0003339985860000044
the average resource score of all the resource individuals in the secondary peripheral resource classification with the number j of the house cell with the number i,
Figure BDA0003339985860000045
the total number of the resource individuals in the secondary peripheral resource classification with the serial number of j of the house cell with the serial number of i; obtaining the quantity weight of the secondary peripheral resource classification based on the total number of the resource individuals in the secondary peripheral resource classification, and for the secondary peripheral resource classification of the forward resource type:
Figure BDA0003339985860000046
for the secondary peripheral resource classification of the negative-going resource type:
Figure BDA0003339985860000047
wherein the content of the first and second substances,
Figure BDA0003339985860000048
the quantity weight of the secondary peripheral resource classification with the serial number j of the house cell with the serial number i; obtaining a weighted average score based on the average resource score of the secondary peripheral resource classification and the quantity weight:
Figure BDA0003339985860000051
after normalization, the following results were obtained:
Figure BDA0003339985860000052
wherein the content of the first and second substances,
Figure BDA0003339985860000053
the second-level classification index of the second-level peripheral resource classification of the house cell with the number i and the number j.
According to some embodiments, the aforementioned method further comprises: carrying out quantity statistics on the prior scores; and obtaining the secondary exponential weight based on the result of the prior scoring quantity statistics.
According to some embodiments, the aforementioned method further comprises:
Figure BDA0003339985860000054
wherein the content of the first and second substances,
Figure BDA0003339985860000055
the primary classification index, q, of the primary peripheral resource classification numbered q for the housing cell numbered ijThe number of secondary peripheral resource classifications under the primary peripheral resource classification numbered q,
Figure BDA0003339985860000056
the second-level exponential weight of the second-level peripheral resource classification with the number j under the first-level peripheral resource classification with the number q.
According to some embodiments, the aforementioned method further comprises: obtaining an influence index of the resource individual according to the first-level peripheral resource classification and the distance information; obtaining a comprehensive index of the resource individual according to the influence index and the comprehensive information; obtaining a first category resource comprehensive index of the housing district according to the plurality of comprehensive indexes and the first-level peripheral resource classification; obtaining a first housing district peripheral resource index according to the first category resource comprehensive index and the first category weight index; and obtaining the peripheral resource index of the second house cell according to the first class resource comprehensive index, the second-level peripheral resource classification and the second class weight index.
According to some embodiments, the aforementioned method further comprises: and adding the peripheral resource index of the first house cell and the peripheral resource index of the second house cell to the house resource endowment map.
According to some embodiments, the aforementioned method further comprises: calculating to obtain a comprehensive index of the surrounding resources of the housing district based on the surrounding resource index of the first housing district and the surrounding resource index of the second housing district; and increasing the comprehensive index of the peripheral resources of the housing district to the endowment map of the housing resources.
According to some embodiments, the method further comprises establishing a correlation between the house resource innate map and the lease price.
According to an aspect of the present application, a method for providing house resource information is provided, including: collecting big data of house resources; building a house resource endowment map by using the house resource big data, wherein the house resource endowment map comprises house cell resource index information; acquiring user attention information; and according to the user attention information, visually presenting a corresponding house resource endowment map on a map.
According to some embodiments, the aforementioned method further comprises: and acquiring peripheral resource data of the housing cell according to preset peripheral resource classification information by utilizing a directional information acquisition technology, wherein the peripheral resource data comprises the peripheral resource classification information.
According to some embodiments, the aforementioned method further comprises: preprocessing the peripheral resource data to obtain preprocessed data; and constructing a house resource intrinsic map based on the preprocessed data, wherein the house resource intrinsic map comprises house cell resource index information.
According to some embodiments, the aforementioned method further comprises: primary peripheral resource classification and secondary peripheral resource classification.
According to some embodiments, the aforementioned method further comprises: obtaining the position information of a housing cell; and obtaining a plurality of resource individual information around the housing district based on the position information of the housing district and the peripheral resource classification information, wherein each resource individual information comprises the classification information of the resource individual, the position information of the resource individual, the distance information of the resource individual and the comprehensive information of the resource individual. The classification information of the resource individuals belongs to the peripheral resource classification information.
According to some embodiments, the aforementioned method further comprises: obtaining an influence index of the resource individual according to the first-level peripheral resource classification and the distance information; obtaining a comprehensive index of the resource individual according to the influence index and the comprehensive information; obtaining a first category resource comprehensive index of the housing district according to the plurality of comprehensive indexes and the first-level peripheral resource classification; obtaining a first housing district peripheral resource index according to the first category resource comprehensive index and the first category weight index; and obtaining the peripheral resource index of the second house cell according to the first class resource comprehensive index, the second-level peripheral resource classification and the second class weight index.
According to some embodiments, the aforementioned method further comprises: and adding the peripheral resource index of the first house cell and the peripheral resource index of the second house cell to the house resource endowment map.
According to some embodiments, the aforementioned method further comprises: calculating to obtain a comprehensive index of the surrounding resources of the housing district based on the surrounding resource index of the first housing district and the surrounding resource index of the second housing district; and increasing the comprehensive index of the peripheral resources of the housing district to the endowment map of the housing resources.
According to some embodiments, the aforementioned method further comprises: the acquisition module acquires peripheral resource data of the housing cell by using a directional information acquisition technology, wherein the peripheral resource data comprises the peripheral resource classification information; the preprocessing module is used for preprocessing the peripheral resource data to obtain preprocessed data; and the building module is used for building a house resource endowment map based on the preprocessed data, wherein the house resource endowment map comprises house cell resource index information.
According to an aspect of the present application, an apparatus for providing premise resource information is provided, including: the acquisition module is used for acquiring house resource big data; the building module is used for building a house resource endowment map by using the house resource big data, wherein the house resource endowment map comprises house cell resource index information; the acquisition module acquires user attention information; and the visualization module is used for visually presenting the corresponding house resource endowment map on the map according to the user attention information.
According to an aspect of the present application, an electronic device is provided, including: one or more processors; storage means for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a method as in any preceding claim.
The beneficial effect of this application:
according to some embodiments, the method provided by the application obtains the index of the peripheral resource by obtaining the peripheral resource data of the housing district, so that the peripheral resource distribution condition of the housing district can be visually presented.
According to some embodiments, the method provided by the application constructs a complete room intrinsic index evaluation system, and can provide reference for the government to determine the price of the rental room and make relevant policies.
According to some embodiments, the system for evaluating the intrinsic performance index of a house constructed by the application can also provide reference for investment estimation of projects and marketing promotion of projects.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without exceeding the protection scope of the present application.
Fig. 1 shows a flowchart of a big-data-based method for assessing an intrinsic index of a room, according to an embodiment.
Fig. 2 shows a flowchart of a big-data-based method for assessing an intrinsic index of a room, according to an embodiment.
Fig. 3 illustrates a data preprocessing flow diagram of a big-data-based room intrinsic index assessment method according to an embodiment.
Fig. 4-a illustrates a computational flow diagram of a big data based room intrinsic index assessment method according to an example embodiment.
Fig. 4-b illustrates a computational flow diagram of another method for assessment of intrinsic indices of a room based on big data according to an example embodiment.
Fig. 5 shows a flowchart of a big-data-based room intrinsic index assessment method according to an example embodiment.
Fig. 6 illustrates a block diagram of an apparatus for constructing a house resource endowment map according to an exemplary embodiment.
Fig. 7 shows a block diagram of an apparatus for providing premise resource information according to an example embodiment.
FIG. 8 shows a block diagram of an electronic device according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the embodiments of the disclosure can be practiced without one or more of the specific details, or with other means, components, materials, devices, or the like. In such cases, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Fig. 1 shows a flowchart of a big-data-based method for assessing an intrinsic index of a room, according to an embodiment.
According to one embodiment, the method disclosed herein may be applied to, for example, a real estate transaction platform.
According to an embodiment, the property trading platform is a network platform that collects property information to be traded and trading functions, wherein the property information to be traded can include the intrinsic atlas of resources around the cell, and the trading functions can include buying, selling and leasing functions. The real estate transaction platform collects and records basic information (such as cell names, building time, position coordinates and the like) of all cells in a city and information (such as resource classification, resource individuals, position coordinates and the like) of peripheral resources of all cells in the city through a big data technology, then obtains resource intrinsic indexes of each cell through calculation processing, and combines the resource intrinsic indexes with an electronic map to visually present the resource intrinsic maps of the corresponding cells.
As shown in fig. 1, in S101, the data of the resources around the residential cell is obtained by using the directional acquisition technique.
According to an example embodiment, the relevant information of the target house cell and its surrounding resources may be obtained from a specified website, such as a map data website, a real estate information website, etc., by a targeted information search technique, such as a targeted crawler, text analysis, multidimensional retrieval, etc.
According to an example embodiment, the obtained relevant information of the target house cell may include, for example: information such as a cell name, construction time, position coordinates and the like; the information about the resources around the target residential cell may include, for example: the method comprises the following steps that a plurality of resource individual information around a housing district, wherein each resource individual information comprises classification information of resource individuals, position information of the resource individuals, distance information of the resource individuals and comprehensive information of the resource individuals, and the classification information of the resource individuals belongs to peripheral resource classification information. The obtaining method may be, for example, taking the target house cell as a center, obtaining the coordinates of each resource individual under the classification according to the resource individual classification search within a distance range, obtaining the related information of each resource individual, and calculating the distance from the target house cell according to the coordinates thereof, for example, obtaining the coordinates of each bus stop belonging to the type of "bus stop" within the range of 1500 meters around the cell, calculating the distance from the target house cell, and obtaining the stop name, the covered bus route, and the like.
According to an exemplary embodiment, the aforementioned individual classification information of peripheral resources may further include a primary peripheral resource classification and a secondary peripheral resource classification, wherein the primary peripheral resource classification is an intrinsic resource classification, and the secondary peripheral resource classification is a specific resource type. According to one embodiment, for a specific resource type "convenience store", the resource is classified as "shopping", i.e., "convenience store" belongs to the primary surrounding resource classification, and "shopping" belongs to the secondary surrounding resource classification. Reference may be made to the house cell perimeter resource classification system of table 1.
Figure BDA0003339985860000101
TABLE 1 House district peripheral resource classification system
In S103, the peripheral resource data is preprocessed to obtain preprocessed data.
According to an example embodiment, the obtained information related to the target residential cell and the surrounding resources thereof may be subjected to preliminary data preprocessing, and valid data is retained, invalid data and duplicate data are removed by using methods such as data cleaning and data correction. According to an embodiment, after related information is collected on a specified website by adopting a directional information search technology, data items required on a page can be analyzed through a regular expression or an xpath and stored in an excel table for cleaning. According to one embodiment, the missing data can be processed by using methods such as repeated, deleted field, and data complement, respectively. For example, fields between rows may be compared, and if the fields are all the same, the fields are identified as duplicate data, and only one of the data is reserved. Non-residential data, such as "office", "factory", etc., may also be deleted, for example, via a "house use" field.
According to an exemplary embodiment, the data after the preliminary data preprocessing is calculated to obtain a plurality of index data, and the detailed steps are described with reference to the embodiments of fig. 2 and 3 below.
At S105, a house resource endowment map is constructed based on the preprocessed data.
According to the embodiment, a house leasing resource endowment result is generated according to the first house cell peripheral resource index and the second house cell peripheral resource index and is displayed on a house property trading platform; the method comprises the steps that through checking marked points on a map, different colors are distinguished, the resource endowment results of a target house cell are checked, and the result interval [0,1] is closer to 1, and the resource endowment is better; the method comprises the steps that according to a resource endowment result platform of a target house cell, house lease price distribution is presented; and combined with the technical processing of a big data platform, the data is presented to the user.
Fig. 2 shows a flowchart of a big-data-based method for assessing an intrinsic index of a room, according to an embodiment.
In S201, based on the distance information of the resource individuals and the corresponding secondary peripheral resource classifications, an effective distance corresponding to the secondary peripheral resource classifications is obtained.
According to an embodiment, the distance information of the resource individual is an actual path distance from the coordinates of the house cell to the coordinates of the resource individual, that is, an optimal actual distance reachable by using a transportation means, which may be referred to as an actual distance for short. The distance information of the resource individual can be determined, for example, from navigation data of a map data provider.
According to an embodiment, the effective distance corresponding to the secondary peripheral resource classification refers to a maximum actual path distance of the residents of the residential district required by the peripheral resources of the residential district, that is, the peripheral resources whose actual path distance exceeds the effective distance are not considered as the peripheral resources of the residential district. The effective distance needs to be calculated, and the method is shown later.
According to an exemplary embodiment, distance information for each individual resource and a corresponding secondary peripheral resource classification are first obtained. And then obtaining the prior effective distance of each secondary peripheral resource classification.
According to one embodiment, the prior effective distance is an effective distance of different secondary peripheral resource classifications, which is obtained by expert's prior assessment.
According to an exemplary embodiment, under the same secondary peripheral resource classification, the cumulative distribution of the distance information of resource individuals within the prior effective distance is calculated:
Figure BDA0003339985860000121
wherein the content of the first and second substances,
Figure BDA0003339985860000122
the a priori effective distance of the secondary peripheral resource classification numbered j,
Figure BDA0003339985860000123
and the distance information of the resource individual with the number of k in the secondary peripheral resource classification with the number of j of the house cell with the number of i.
According to an exemplary embodiment, the following steps are performed under the same secondary peripheral resource classification:
s1: and quantitatively increasing the prior effective distance of the secondary peripheral resource classification, judging whether the increased prior effective distance is greater than the administrative division radius, and executing S4 if the increased prior effective distance is greater than the administrative division radius.
According to an embodiment, the prior effective distance of the secondary peripheral resource classification is quantitatively increased by a length, which may be determined as follows: and finding the resource individual with the minimum actual distance under the corresponding secondary peripheral resource classification, and taking the distance information of the resource individual, namely the actual distance between the resource individual and the house cell as the length of the quantitative increase.
Optionally, for convenience of calculation, a value obtained by rounding up the minimum distance information in percentile may be used as the length of the quantitative increase:
Figure BDA0003339985860000124
wherein the content of the first and second substances,
Figure BDA0003339985860000125
namely the minimum value in the distance information of all resource individuals of the secondary peripheral resource classification with the serial number of j of the house cell with the serial number of i.
According to an embodiment, determining whether the increased a priori effective distance is greater than the administrative division radius means: and after the prior effective distance is increased, judging whether a circular area which takes the target house cell as the center and takes the increased prior effective distance as the radius exceeds an administrative division where the house cell is located. The administrative division may be an administrative division such as a district, a county, a city, a province, or the like, and may be specifically determined according to a usage scenario.
S2: and calculating the cumulative distribution of the distance information of the resource individuals in the increased prior effective distance.
According to one embodiment, the effective distance is calculated after each increase
Figure BDA0003339985860000126
Cumulative distribution of (2):
Figure BDA0003339985860000127
wherein n is the nth quantitative increase prior effective distance, and n is 1, 2.
S3: comparing the difference between the newly obtained cumulative distribution and the cumulative distribution obtained last time, and judging whether the difference is significant, if so, taking the newly obtained prior effective distance as the effective distance, and if not, executing S1.
According to an embodiment, the Kolmogorov-Smirnov method may be used to test whether there is a significant difference in the cumulative distribution of distance information for resource individuals within a priori effective distances after increasing the priori effective distances.
S4: and taking the previous effective distance as the effective distance.
In S203, the resource individual information is screened based on the effective distance to obtain the preprocessed data.
According to the embodiment, the resource individual information of the resource individual within the effective distance is screened to obtain the preprocessed data. Namely, after the effective distance is obtained, the previously selected resource individuals are further screened, and the resource individuals with the distance information within the effective distance are used as final processing objects and as preprocessing data.
In S205, a secondary classification index corresponding to the secondary peripheral resource classification is obtained based on the preprocessed data.
According to the embodiment, the distance information of the resource individuals belonging to the same secondary peripheral resource classification is sorted in ascending order, and a decile number is obtained;
according to an example embodiment, the secondary peripheral resource classifications are further classified as either positive or negative resource types.
According to one embodiment, the forward resource type is a secondary category of surrounding resources having a positive impact on living, and refer to the contents of table 1, such as convenience stores, hospitals, etc. The closer the resource individuals of the forward resource type are to the house cell, the better.
According to another embodiment, the negative resource type is a secondary peripheral resource classification having a negative impact on occupancy, such as a refuse terminal, a cemetery, etc., with reference to the contents of Table 1. The closer the resource individuals of the negative resource type are to the house cell, the worse.
According to one embodiment, for resource individuals in the secondary peripheral resource classification of the forward resource type, the resource score of the resource individual is calculated by the following formula:
Figure BDA0003339985860000141
according to another embodiment, for the individual resource in the secondary peripheral resource classification of a negative resource type, the resource score of the individual resource is calculated using the following equation:
Figure BDA0003339985860000142
wherein the content of the first and second substances,
Figure BDA0003339985860000143
resource score of resource individual numbered k in secondary peripheral resource classification numbered j of house cell numbered i, djnAnd n is 0,1,2,3, 9, which is a decile number numbered n in the distance information of all the resource individuals in ascending order in the secondary peripheral resource classification numbered j.
According to an exemplary embodiment, the average resource score of all resource individuals in the same secondary peripheral resource classification is then obtained based on the resource scores of the resource individuals, that is, for each secondary peripheral resource classification, the scores of all resource individuals therein are summed and divided by the total number of these resource individuals:
Figure BDA0003339985860000144
wherein the content of the first and second substances,
Figure BDA0003339985860000145
is the average resource score of all resource individuals in the secondary peripheral resource classification with the serial number of j of the house cell with the serial number of i,
Figure BDA0003339985860000146
the total number of resource individuals in the secondary peripheral resource classification with the number j of the house cell with the number i.
According to the exemplary embodiment, then, based on the total number of resource individuals in the secondary peripheral resource classification, the number weight of the secondary peripheral resource classification is obtained, and for the secondary peripheral resource classification of the forward resource type:
Figure BDA0003339985860000147
for secondary peripheral resource classifications of negative resource types:
Figure BDA0003339985860000151
wherein the content of the first and second substances,
Figure BDA0003339985860000152
the number of secondary peripheral resource classifications numbered j for the house cell numbered i is weighted.
According to one embodiment, the quantitative weight reflects the proportion of the secondary peripheral resource classification in the whole peripheral resources.
According to an exemplary embodiment, a weighted average score is finally obtained based on the average resource score of the secondary peripheral resource classification and the quantitative weight:
Figure BDA0003339985860000153
after normalization, the following results were obtained:
Figure BDA0003339985860000154
wherein the content of the first and second substances,
Figure BDA0003339985860000155
the second-level classification index of the second-level peripheral resource classification of the house cell with the number i and the number j. The higher the secondary classification index is, the more ideal the distribution of the secondary peripheral resource classification representing the corresponding house cell is.
In S207, a secondary index weight corresponding to the secondary classification index is obtained based on the prior score obtained in advance.
According to one embodiment, the secondary exponential weighting reflects the degree of importance of each secondary peripheral resource classification in all secondary peripheral resource classifications, which is derived based primarily on the prior scores.
The prior score obtained in advance is the score obtained by the expert evaluating the secondary peripheral resource classification in advance.
According to one embodiment, for example, the score for classifying the secondary peripheral resources may be set as w, and the score range is 1-9, so that w is {1, 2. A larger w indicates that the corresponding secondary peripheral resource classification is more important.
According to one embodiment, firstly, the prior scores need to be subjected to quantitative statistics, that is, the number of scoring experts which give each score is counted:
Figure BDA0003339985860000161
wherein the content of the first and second substances,
Figure BDA0003339985860000162
number of experts scored as w within the class of first-level peripheral resources numbered q, qjThe number of secondary peripheral resource classifications within the primary peripheral resource classification numbered q,
Figure BDA0003339985860000163
the secondary peripheral resource category, numbered j, scores the number of experts in w.
According to one embodiment, the second-order exponential weight is obtained based on the result of the prior score number statistics, wherein
Figure BDA0003339985860000164
Width, also called score w, whose score rank interval is:
Figure BDA0003339985860000165
wherein the content of the first and second substances,
Figure BDA0003339985860000166
further, the average rank of the scores is calculated:
Figure BDA0003339985860000167
further, the rank sum of the secondary peripheral resource classification numbered j in the primary peripheral resource classification numbered q is solved:
Figure BDA0003339985860000168
finally, normalizing the rank effect of the secondary peripheral resource classification to obtain a secondary index weight of each secondary peripheral resource classification as follows:
Figure BDA0003339985860000169
wherein the content of the first and second substances,
Figure BDA00033399858600001610
and the second-level exponential weight of the second-level peripheral resource classification with the number j in the first-level peripheral resource classification with the number q.
In S209, a primary classification index corresponding to the primary peripheral resource classification is obtained based on the secondary classification index and the corresponding secondary index weight.
According to one embodiment, for a class one peripheral resource classification, the sum of all class two indices multiplied by the corresponding class two index weight products is its class one index:
Figure BDA0003339985860000171
wherein the content of the first and second substances,
Figure BDA0003339985860000172
a primary classification index of a primary peripheral resource classification numbered q for a residential district numbered i, qjThe number of secondary peripheral resource classifications under the primary peripheral resource classification numbered q,
Figure BDA0003339985860000173
and the second-level exponential weight of the second-level peripheral resource classification with the number j under the first-level peripheral resource classification with the number q.
Fig. 3 illustrates a data preprocessing flow diagram of a big-data-based room intrinsic index assessment method according to an embodiment.
In S401, an influence index of the resource individual is obtained according to the primary peripheral resource classification and the distance information.
According to an example embodiment, the data of the resource individuals obtained by the directional information search in the previous step from the cell may be grouped according to the class of the peripheral resource to which the data belongs, and the influence index of each resource individual is calculated through the data set in each group. The calculation methods are different according to the nature of the primary peripheral resource classification, and the detailed calculation steps are described with reference to the embodiments of fig. 4-a and 4-b.
In S403, the comprehensive index of the resource individual is obtained according to the influence index and the comprehensive information.
According to the embodiment, the comprehensive index of the individual peripheral resources is calculated and obtained based on the influence index of each individual peripheral resource and other comprehensive information of the corresponding individual peripheral resources. According to an embodiment, taking a 'bus station' as an example, the method comprises the following steps:
firstly, referring to a forward index calculation process to obtain the individual influence index of each peripheral resource.
And secondly, according to other comprehensive information of each station, obtaining another measuring coefficient for the number of the bus lines passing by the station and the number of all the bus lines in the city in the embodiment. Specifically, the number of all the bus lines in the city is obtained through statistics, and assuming that a certain city has 200 lines and the number of lines at a certain stop is 10, the weighting coefficient of the stop is 10/200-0.05.
And thirdly, multiplying each individual influence index of the peripheral resources by a corresponding station line measurement coefficient to obtain a comprehensive index of the individual peripheral resources, such as 0.999 by 0.05 to 0.0499.
At S405, the first category resource comprehensive index of the housing district is obtained according to the plurality of comprehensive indexes and the first-level peripheral resource classification.
According to the embodiment of the embodiment, for each cell, the single index indexes of the cell are summarized according to the individual comprehensive indexes of the peripheral resources belonging to the same first category, so that the first category comprehensive index of the peripheral resources is obtained. According to an embodiment, taking a bus stop as an example, the method comprises the following steps:
in the first step, for example, a certain cell has 6 bus stops, and the individual comprehensive index of the peripheral resources of each stop is calculated to obtain 0.0499, 0.1021, 0.9812 and 0.5678. The individual comprehensive indexes of the peripheral resources are summed up to 0.0499+0.1021+0.9812+0.5678 ═ 1.701
Secondly, recalculating the individual comprehensive indexes of the peripheral resources of all the cells by using a normalization function to obtain a first-class comprehensive index of the peripheral resources, and enabling the first-class comprehensive indexes of all the peripheral resources to fall between intervals of [0,1 ]: (X-min)/(max-min).
In step S407, a first house cell peripheral resource index is obtained according to the first category resource comprehensive index and the first category weight index.
According to an exemplary embodiment, the first residential cell surrounding resource index is obtained according to the aforementioned surrounding resource first category comprehensive index and first category weighting index. The first class weight index is obtained by calculating a directed rank weight statistical method for each class of the first-class peripheral resources. According to one embodiment, the method comprises the steps of:
firstly, multiplying the first-class comprehensive index of the peripheral resources of each first-class peripheral resource classification of each cell by the corresponding first-class weight index.
And step two, accumulating the calculation results of the step one to obtain the peripheral resource index of each first house cell.
Thirdly, converting the peripheral resource indexes of the first housing cells by using a normalization function, so that the peripheral resource indexes of all the first housing cells fall between [0,1 ]: (X-min)/(max-min).
In S409, a second housing cell peripheral resource index is obtained according to the first category resource comprehensive index, the second-level peripheral resource classification, and the second category weight index.
According to an exemplary embodiment, the peripheral resource index of the second house cell is obtained according to the aforementioned comprehensive index of the first class of peripheral resources, the classification of the secondary peripheral resources, and the weighting index of the second class. The second category weight index is obtained by calculating a directed rank weight statistical method for each secondary peripheral resource classification. According to one embodiment, the method comprises the steps of:
first, the secondary indicators of each cell are calculated according to the above method, such as shopping (convenience store 0.8298 supermarket 0.5342, farmer market 0.25)
Step two, multiplying the weight corresponding to the secondary index by the first class comprehensive index of the peripheral resource
(0.8298*0.315657+0.5342*0.383697+0.25*0.300645)
＝0.2619+0.205+0.0752＝0.5421
Thirdly, performing normalization function conversion on the obtained second house cell peripheral resource indexes to enable all second house cell peripheral resource indexes to fall between [0,1 ]: (X-min)/(max-min).
According to an example embodiment, the first house cell peripheral resource index and the second house cell peripheral resource index may be further added to the house resource endowment map.
According to an example embodiment, the comprehensive index of the resources around the housing cell may be obtained by calculation based on the index of the resources around the first housing cell and the index of the resources around the second housing cell, so that the comprehensive condition of the resources around the cell may be directly evaluated through the index; and increasing the comprehensive index of the peripheral resources of the housing district to the endowment atlas of the housing resources.
Fig. 4-a illustrates a computational flow diagram of a big data based room intrinsic index assessment method according to an example embodiment.
According to an example embodiment, the peripheral resource individual influence index may be calculated after the peripheral resource individual distance information belonging to the same one-level classification is arranged in order according to the one-level peripheral resource classification. The peripheral resources are further classified into positive-impact resources and negative-impact resources according to the grades thereof, the positive-impact resources are the peripheral resources which have positive impact on the lives of residents, the negative-impact resources are the peripheral resources which have negative impact on the lives of the residents, for example, a bus stop is the positive-impact resources, and a garbage station is the negative-impact resources.
According to one embodiment, a positive impact resource individual classified as a "bus stop" for example is processed by using a primary peripheral resource of a target housing cell, and the process is as follows:
s401, sorting the distances of all bus stations of the target house cell obtained by directional search from small to large.
And S403, performing decimal place calculation on all the distances by using a mathematical statistical method, namely summarizing the number of the distances, and averagely dividing the number into 10 parts, wherein the number of each part is the same. For example, the following series of distance numbers [ 20, 50, 100, 120, 160, 300, 320, 400, 410, 420, 500, 560, 600, 620, 650, 660, 680, 700, 750, 800 ] are decile processed to obtain [ 20, 50 ], [ 100, 120 ], [ 160, 300 ] … [ 750, 800 ], the omission terms and the like, wherein the maximum value for each group is the decile of the group, e.g., 50 is the decile of the first group and 120 is the decile of the second group.
S405, index estimation values 0.9 are distributed to the first group of deciles, index estimation values 0.8 are distributed to the second group of deciles, and the like are repeated, and the last group is 0.
S407, performing subgroup analysis on the distance in each decile, taking the first group [ 20, 50 ] as an example, and calculating the exponential estimation value of 20 therein as follows: 0.9+ (50-20)/(50-20) × 0.1 ═ 1. For the calculation of 50: 0.9+ (50-50)/(50-20) × 0.1 ═ 0.9, where the numerator (50-50) represents the decile 50 of the group to which the current distance value belongs minus the current distance value 50, and the denominator (50-20) represents the decile 50 of the first group minus the minimum value 20. The decimals of the previous group are subtracted from the decimals of the second group representing the current group. And obtaining the estimation value of each peripheral resource individual influence index from the first group to the ninth group by adopting the calculation method.
A tenth group of calculations is given as [ 750, 800 ] for example, where the calculation formula for 800 is: 0+ (800+1-800)/(800+1-700) × 0.1 ═ 0.0099.
Where the numerator (800+1-700) represents the decile 800 of the last group minus the decile 700 of the previous group, plus 1 to account for a difference from 0; the denominator is added with 1 according to the numerical calculation rule.
Fig. 4-b illustrates a computational flow diagram of another method for assessment of intrinsic indices of a room based on big data according to an example embodiment.
In view of the above, according to another embodiment, the negative impact resource individual classified as "garbage station" for example as the primary peripheral resource of a target residential district is processed by the following processes:
s402, sorting the distances of all garbage stations of the target house cell obtained by directional search from small to large.
S404, performing decimal place calculation on all the distances by a mathematical statistical method, namely summarizing the number of the distances, and averagely dividing the number into 10 parts, wherein the number of each part is the same. For example, the following series of distance numbers [ 20, 50, 100, 120, 160, 300, 320, 400, 410, 420, 500, 560, 600, 620, 650, 660, 680, 700, 750, 800 ] are decile processed to obtain [ 20, 50 ], [ 100, 120 ], [ 160, 300 ] … [ 750, 800 ], the omission terms and the like, wherein the maximum value for each group is the decile of the group, e.g., 50 is the decile of the first group and 120 is the decile of the second group.
S406, index estimation values 0 are distributed to the first group of deciles, index estimation values 0.1 are distributed to the second group of deciles, and the like are repeated, and the number of the last group is 0.9.
S408, performing subgroup analysis on the distance in each decile, taking the first group (20, 50) as an example, and calculating the exponential estimation value of 20 as follows: 0+ (20-20)/(50-20) × 0.1 ═ 0. For the calculation of 50: 0+ (50-20)/(50-20) × 0.1 ═ 0.1, where the numerator (50-20) represents the current distance value 50 minus the minimum value of the assigned group 20 and the denominator (50-20) represents the decile of the first group 50 minus the minimum value of 20. The decimals of the previous group are subtracted from the decimals of the second group representing the current group. And obtaining the estimation value of each peripheral resource individual influence index from the first group to the ninth group by adopting the calculation method.
A tenth group of calculations is given as [ 750, 800 ] for example, where the calculation formula for 800 is: 0.9+ (800-700)/(800+1-700) × 0.1-0.9990. Wherein the molecule (800-700) represents the decile 800 of the last group minus the decile 700 of the previous group; the denominator (800+1-700) represents the range of differences between the last set of decimals 800 and the previous set of decimals 700, plus 1 to allow the exponent to approach 1 indefinitely.
Fig. 5 shows a flowchart of a big-data-based room intrinsic index assessment method according to an example embodiment.
At S501, house resource big data is collected.
According to an example embodiment, the process of acquiring the house big data integrates the aforementioned directional acquisition technology to acquire the surrounding resource data of the house cell and preprocess the surrounding resource data to obtain preprocessed data, which is not described herein again.
And S503, constructing the house resource intrinsic map by using the house resource big data.
According to the exemplary embodiment, the building of the house resource innate map by using the house resource big data is consistent with the building of the house resource innate map based on the preprocessed data, and will not be described herein again.
In S505, user attention information is acquired.
According to the example embodiment, the user can search the needed housing cell according to the requirement condition of the peripheral resource of the housing cell on the house trading platform, or check the peripheral resource information of a certain cell.
According to another exemplary embodiment, the property trading platform can visually present visual target houses, resource endowment maps of cells or house search results to the user through, for example, an electronic map according to the attention information of the user, for example, houses, cells selected by the user or results of the user performing the selection of the endowment indexes on one or more resource types.
And S507, according to the attention information of the user, visually presenting the corresponding house resource endowment map on the map.
According to the embodiment, a house leasing resource endowment result is generated according to the first house cell peripheral resource index and the second house cell peripheral resource index and is displayed on a house property trading platform; the method comprises the steps that through checking marked points on a map, different colors are distinguished, the resource endowment results of a target house cell are checked, and the result interval [0,1] is closer to 1, and the resource endowment is better; the method comprises the steps that according to a resource endowment result platform of a target house cell, house lease price distribution is presented; and combined with the technical processing of a big data platform, the data is presented to the user.
According to another exemplary embodiment, the obtained resource endowment index can also be used as a basis for pricing house rent, so that the pricing dimension of the house rent is more comprehensive, and the pricing result is more scientific and reasonable.
Fig. 6 illustrates a block diagram of an apparatus for constructing a house resource endowment map according to an exemplary embodiment.
Referring to fig. 6, the apparatus for constructing the intrinsic spectrum of the house resource is composed of an acquisition module 601, a preprocessing module 603, and a construction module 605, wherein:
an acquisition module 601, configured to acquire peripheral resource data of a residential community according to preset peripheral resource classification information by using a directional information acquisition technology, where the peripheral resource data includes the peripheral resource classification information;
the preprocessing module 603 is used for preprocessing the peripheral resource data to obtain preprocessed data;
a construction module 605, which constructs a house resource endowment map based on the preprocessed data, wherein the house resource endowment map comprises house cell resource index information.
Fig. 7 shows a block diagram of an apparatus for providing premise resource information according to an example embodiment.
Referring to fig. 7, the apparatus for providing the house resource information is composed of an acquisition module 701, a construction module 703, an acquisition module 705 and a visualization module 707, wherein the acquisition module 705 and the visualization module 707 are respectively connected to the acquisition module and the visualization module
The acquisition module 701 acquires house resource big data;
the building module 703 is configured to build a house resource intrinsic map by using the house resource big data, where the house resource intrinsic map includes house cell resource index information;
an obtaining module 705 for obtaining the user attention information;
and the visualization module 707 is used for visually presenting the corresponding house resource endowment map on the map according to the user attention information.
FIG. 8 shows a block diagram of an electronic device according to an example embodiment.
An electronic device 800 according to this embodiment of the application is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one memory unit 820, a bus 830 connecting the various system components (including the memory unit 820 and the processing unit 810), a display unit 840, and the like.
Wherein the storage unit stores program code, which can be executed by the processing unit 810, to cause the processing unit 810 to perform the methods according to various exemplary embodiments of the present application described herein. For example, the processing unit 810 may perform the methods described above.
The storage unit 820 may include readable media in the form of volatile memory units such as a random access memory unit (RAM)8201 and/or a cache memory unit 8202, and may further include a read only memory unit (ROM) 8203.
The storage unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 830 may be any of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 800 may also communicate with one or more external devices 8001 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 800, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 800 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 860. The network adapter 860 may communicate with other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. The technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiments of the present application.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions described above.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present application.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the description of the embodiments is only intended to facilitate the understanding of the methods and their core concepts of the present application. Meanwhile, a person skilled in the art should, according to the idea of the present application, change or modify the embodiments and applications of the present application based on the scope of the present application. In view of the above, the description should not be taken as limiting the application.

Claims (26)

1. A method for constructing a house resource endowment map is characterized by comprising the following steps:
acquiring peripheral resource data of the housing cell by using a directional information acquisition technology, wherein the peripheral resource data comprises peripheral resource classification information;
preprocessing the peripheral resource data to obtain preprocessed data;
and constructing a house resource intrinsic map based on the preprocessed data, wherein the house resource intrinsic map comprises house cell resource index information.
2. The method of claim 1, wherein said obtaining ambient resource data for the premise cell comprises:
obtaining the position information of a housing cell;
obtaining a plurality of resource individual information around the housing district based on the position information of the housing district and the surrounding resource classification information, wherein the resource individual information comprises the classification information of resource individuals, the position information of the resource individuals, the distance information of the resource individuals and the comprehensive information of the resource individuals, and the classification information of the resource individuals is from the surrounding resource classification information.
3. The method of claim 2, wherein the ambient resource classification information comprises:
the method comprises a primary peripheral resource classification and a secondary peripheral resource classification, wherein the secondary peripheral resource classification is a secondary classification under the primary peripheral resource classification.
4. The method of claim 3, wherein the pre-processing the peripheral resource data to obtain pre-processed data comprises:
obtaining an effective distance corresponding to the secondary peripheral resource classification based on the distance information of the resource individuals and the corresponding secondary peripheral resource classification;
and screening the resource individual information based on the effective distance to obtain preprocessed data.
5. The method of claim 4, wherein obtaining the effective distance corresponding to the secondary peripheral resource classification based on the distance information of the resource individuals and the corresponding secondary peripheral resource classification comprises:
obtaining distance information of each resource individual and corresponding secondary peripheral resource classification;
obtaining the prior effective distance of each secondary peripheral resource classification;
under the same secondary peripheral resource classification, calculating the cumulative distribution of the distance information of the resource individuals in the prior effective distance:
Figure FDA0003339985850000021
wherein the content of the first and second substances,
Figure FDA0003339985850000022
the a priori effective distance of the secondary peripheral resource classification numbered j,
Figure FDA0003339985850000023
and the distance information of the resource individuals with the number of k in the secondary peripheral resource classification with the number of j of the house cell with the number of i.
6. The method of claim 5, wherein the obtaining the effective distance corresponding to the secondary peripheral resource classification based on the distance information of the resource individuals and the corresponding secondary peripheral resource classification further comprises the steps of:
under the same secondary peripheral resource classification:
s1: quantitatively increasing the prior effective distance of the secondary peripheral resource classification, judging whether the increased prior effective distance is greater than the administrative division radius, and executing S4 if the increased prior effective distance is greater than the administrative division radius;
s2: calculating the cumulative distribution of the distance information of the resource individuals in the increased prior effective distance;
s3: comparing the difference between the newly obtained cumulative distribution and the cumulative distribution obtained at the previous time, judging whether the difference is significant, if so, taking the newly obtained prior effective distance as the effective distance, and if not, executing S1;
s4: and taking the previous priori effective distance as the effective distance.
7. The method of claim 6, wherein the screening the resource individual information based on the effective distance to obtain pre-processed data comprises:
and screening the resource individual information of the distance information of the resource individual within the effective distance to obtain the preprocessed data.
8. The method of claim 7, wherein said constructing a house resources endowment map based on said preprocessed data comprises:
obtaining a secondary classification index corresponding to the secondary peripheral resource classification based on the preprocessed data;
obtaining a second-level index weight corresponding to the second-level classification index based on a priori score obtained in advance;
and obtaining a primary classification index corresponding to the primary peripheral resource classification based on the secondary classification index and the corresponding secondary index weight, wherein the secondary classification index and the primary classification index are components of the resource index information of the housing district.
9. The method of claim 8, wherein obtaining a secondary classification index corresponding to the secondary peripheral resource classification based on the preprocessed data comprises:
sorting the distance information of the resource individuals belonging to the same secondary peripheral resource classification in ascending order to obtain a decile number;
the secondary peripheral resource classification is further classified into a positive resource type or a negative resource type, and for the resource individuals in the secondary peripheral resource classification of the positive resource type, the resource score of the resource individual is calculated:
Figure FDA0003339985850000031
for the resource individuals in the secondary peripheral resource classification of the negative resource type, calculating resource scores of the resource individuals:
Figure FDA0003339985850000032
wherein the content of the first and second substances,
Figure FDA0003339985850000033
number of housing cell with number ij's resource score of said resource individuals numbered k in the secondary peripheral resource classification, djnA decile number with a number of n is included in the distance information of all the resource individuals in ascending order in the secondary peripheral resource classification with a number of j, wherein n is 0,1,2,3,. and 9;
obtaining the average resource score of all resource individuals in the same secondary peripheral resource classification based on the resource scores of the resource individuals:
Figure FDA0003339985850000041
wherein the content of the first and second substances,
Figure FDA0003339985850000042
the average resource score of all the resource individuals in the secondary peripheral resource classification with the number j of the house cell with the number i,
Figure FDA0003339985850000043
the total number of the resource individuals in the secondary peripheral resource classification with the serial number of j of the house cell with the serial number of i;
obtaining the quantity weight of the secondary peripheral resource classification based on the total number of the resource individuals in the secondary peripheral resource classification, and for the secondary peripheral resource classification of the forward resource type:
Figure FDA0003339985850000044
for the secondary peripheral resource classification of the negative-going resource type:
Figure FDA0003339985850000045
wherein the content of the first and second substances,
Figure FDA0003339985850000046
the quantity weight of the secondary peripheral resource classification with the serial number j of the house cell with the serial number i;
obtaining a weighted average score based on the average resource score of the secondary peripheral resource classification and the quantity weight:
Figure FDA0003339985850000047
after normalization, the following results were obtained:
Figure FDA0003339985850000048
wherein the content of the first and second substances,
Figure FDA0003339985850000051
the second-level classification index of the second-level peripheral resource classification of the house cell with the number i and the number j.
10. The method of claim 9, wherein obtaining a second-level index weight corresponding to the second-level classification index based on a priori scores obtained in advance comprises:
carrying out quantity statistics on the prior scores;
and obtaining the secondary exponential weight based on the result of the prior scoring quantity statistics.
11. The method of claim 10, wherein obtaining the primary classification index corresponding to the primary peripheral resource classification based on the secondary classification index and the corresponding secondary index weight comprises:
Figure FDA0003339985850000052
wherein the content of the first and second substances,
Figure FDA0003339985850000053
the primary classification index, q, of the primary peripheral resource classification numbered q for the housing cell numbered ijThe number of secondary peripheral resource classifications under the primary peripheral resource classification numbered q,
Figure FDA0003339985850000054
the second-level exponential weight of the second-level peripheral resource classification with the number j under the first-level peripheral resource classification with the number q.
12. The method of claim 3, wherein the pre-processing the peripheral resource data to obtain pre-processed data comprises:
obtaining an influence index of the resource individual according to the first-level peripheral resource classification and the distance information;
obtaining a comprehensive index of the resource individual according to the influence index and the comprehensive information;
obtaining a first category resource comprehensive index of the housing district according to the plurality of comprehensive indexes and the first-level peripheral resource classification;
obtaining a first housing district peripheral resource index according to the first category resource comprehensive index and the first category weight index;
and obtaining the peripheral resource index of the second house cell according to the first class resource comprehensive index, the second-level peripheral resource classification and the second class weight index.
13. The method of claim 12, wherein said constructing a house resources endowment map based on said preprocessed data comprises:
and adding the peripheral resource index of the first house cell and the peripheral resource index of the second house cell to the house resource endowment map.
14. The method of claim 12, wherein constructing a house resources endowment map based on the pre-processed data further comprises:
calculating to obtain a comprehensive index of the surrounding resources of the housing district based on the surrounding resource index of the first housing district and the surrounding resource index of the second housing district;
and increasing the comprehensive index of the peripheral resources of the housing district to the endowment map of the housing resources.
15. The method of claim 14, further comprising establishing a correlation between the house resource innate map and lease price.
16. A method of providing premise resource information, comprising:
collecting big data of house resources;
building a house resource endowment map by using the house resource big data, wherein the house resource endowment map comprises house cell resource index information;
acquiring user attention information;
and according to the user attention information, visually presenting a corresponding house resource endowment map on a map.
17. The method of claim 16, wherein collecting the premise resource big data comprises:
and acquiring peripheral resource data of the housing cell according to preset peripheral resource classification information by utilizing a directional information acquisition technology, wherein the peripheral resource data comprises the peripheral resource classification information.
18. The method of claim 17, wherein said utilizing said house resource big data to construct a house resource innate map comprises:
preprocessing the peripheral resource data to obtain preprocessed data;
and constructing a house resource intrinsic map based on the preprocessed data, wherein the house resource intrinsic map comprises house cell resource index information.
19. The method of claim 18, wherein the ambient resource classification information comprises:
primary peripheral resource classification and secondary peripheral resource classification.
20. The method of claim 19, wherein said obtaining ambient resource data for the premise cell comprises:
obtaining the position information of a housing cell;
and obtaining a plurality of resource individual information around the housing district based on the position information of the housing district and the peripheral resource classification information, wherein each resource individual information comprises the classification information of the resource individual, the position information of the resource individual, the distance information of the resource individual and the comprehensive information of the resource individual. The classification information of the resource individuals belongs to the peripheral resource classification information.
21. The method of claim 20, wherein said preprocessing said peripheral resource data to obtain preprocessed data comprises:
obtaining an influence index of the resource individual according to the first-level peripheral resource classification and the distance information;
obtaining a comprehensive index of the resource individual according to the influence index and the comprehensive information;
obtaining a first category resource comprehensive index of the housing district according to the plurality of comprehensive indexes and the first-level peripheral resource classification;
obtaining a first housing district peripheral resource index according to the first category resource comprehensive index and the first category weight index;
and obtaining the peripheral resource index of the second house cell according to the first class resource comprehensive index, the second-level peripheral resource classification and the second class weight index.
22. The method of claim 21, wherein said constructing a house resources endowment map based on said preprocessed data comprises:
and adding the peripheral resource index of the first house cell and the peripheral resource index of the second house cell to the house resource endowment map.
23. The method of claim 21, wherein constructing a house resources endowment map based on the pre-processed data further comprises:
calculating to obtain a comprehensive index of the surrounding resources of the housing district based on the surrounding resource index of the first housing district and the surrounding resource index of the second housing district;
and increasing the comprehensive index of the peripheral resources of the housing district to the endowment map of the housing resources.
24. An apparatus for constructing a house resource endowment map, comprising:
the acquisition module acquires peripheral resource data of the housing cell by using a directional information acquisition technology, wherein the peripheral resource data comprises the peripheral resource classification information;
the preprocessing module is used for preprocessing the peripheral resource data to obtain preprocessed data;
and the building module is used for building a house resource endowment map based on the preprocessed data, wherein the house resource endowment map comprises house cell resource index information.
25. An apparatus for providing premise resource information, comprising:
the acquisition module is used for acquiring house resource big data;
the building module is used for building a house resource endowment map by using the house resource big data, wherein the house resource endowment map comprises house cell resource index information;
the acquisition module acquires user attention information;
and the visualization module is used for visually presenting the corresponding house resource endowment map on the map according to the user attention information.
26. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-23.
CN202111305308.3A 2021-11-05 2021-11-05 Method for constructing house resource endowment map and method for providing house resource information Pending CN114186785A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115471367A (en) * 2022-09-28 2022-12-13 浙江臻善科技股份有限公司 House classification system and method for idle agricultural house inventory

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115471367A (en) * 2022-09-28 2022-12-13 浙江臻善科技股份有限公司 House classification system and method for idle agricultural house inventory

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